version 1

app.py

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+import gradio as gr
+import joblib
+
+# Load models and vectorizer from the models folder
+logistic_model = joblib.load("models/best_logistic_model.pkl")
+svm_model = joblib.load("models/best_svc_model.pkl")
+random_forest_model = joblib.load("models/best_rf_model.pkl")
+knn_model = joblib.load("models/best_knn_model.pkl")
+vectorizer = joblib.load("models/vectorizer.pkl")
+
+# Model selection mapping
+models = {
+    "Logistic Regression": logistic_model,
+    "SVM": svm_model,
+    "Random Forest": random_forest_model,
+    "KNN": knn_model,
+}
+
+# Prediction function
+def predict_sentiment(review, model_name):
+    try:
+        if not review.strip():
+            return "Error: Review cannot be empty", None
+
+        if model_name not in models:
+            return "Error: Invalid model selected", None
+
+        # Preprocess the text
+        text_vector = vectorizer.transform([review])
+
+        # Predict using the selected model
+        model = models[model_name]
+        prediction = model.predict(text_vector)[0]
+        probabilities = model.predict_proba(text_vector)[0] if hasattr(model, "predict_proba") else None
+
+        # Format the output
+        sentiment = "Positive Feedback" if prediction == 1 else "Negative Feedback"
+        probabilities_output = (
+            {
+                "Positive": probabilities[1],  # Raw probability (0.0 - 1.0)
+                "Negative": probabilities[0],  # Raw probability (0.0 - 1.0)
+            }
+            if probabilities is not None
+            else "Probabilities not available"
+        )
+
+        return sentiment, probabilities_output
+    except Exception as e:
+        # Log the error to the console for debugging
+        print(f"Error in prediction: {e}")
+        return f"Error: {str(e)}", None
+
+# Create Gradio Interface
+inputs = [
+    gr.Textbox(label="Review Comment", placeholder="Enter your review here..."),
+    gr.Dropdown(choices=["Logistic Regression", "SVM", "Random Forest", "KNN"], label="Model"),
+]
+
+outputs = [
+    gr.Textbox(label="Predicted Sentiment Class"),
+    gr.Label(label="Predicted Probability"),
+]
+
+# Launch Gradio App
+gr.Interface(
+    fn=predict_sentiment,
+    inputs=inputs,
+    outputs=outputs,
+    title="Sentiment Analysis",
+    description="Enter a review and select a model to predict sentiment.",
+).launch()

models/best_knn_model.pkl

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+oid sha256:0ae5bfda964fad7a1d30fe5cdaaa8e9991670fa81ec5644033b3e3e1d08674e3
+size 105844

models/best_logistic_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:62b2ecaa84982c38c3df38e113605ced250896abf1cb6c762c411a88a6cdeaa1
+size 15215

models/best_rf_model.pkl

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+oid sha256:a4d4944dc56c304bda9e45b9b96496e1af61a137ddcae6cc518cc40591514a24
+size 1025289

models/best_svc_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6b2acf2da04be8cb990b9d2fc5573564acdddb7e4b6dfba9c4ff9863193b852f
+size 89531

models/vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4df3665f41c3f8e87d1945f3625acb00d8812f7953e10d20f37e6f99d2cf45cd
+size 21760